PwC - Modelling Olympic performance

As we get closer to the start of the 2012 Olympic Games in London at the end of July, so interest is rising in the likely medal tallies of different countries.

As a contribution to this debate, PwC has conducted an analysis of the key factors of past Olympic performance and used this to produce some benchmarks against which performance at the 2012 Olympics can be judged.

This updates similar analysis we produced around the time of the 2000, 2004 and 2008 Olympics.

So what factors are statistically significant in explaining how the medals are shared out by the competing countries at the Olympic Games? We believe there are four:

PopulationAverage income levelsWhether the country was previously part of the former Soviet/communist bloc (including Cuba and China), andWhether the country is the host nation.In general, the number of medals won increases with the population and economic wealth of the country, but less than proportionately: David can sometimes beat Goliath in the Olympic arena, although superpowers like the US, China and Russia continue to dominate at the top of the medal table.

And could 'home advantage' - enjoyed in the past by both China in Beijing and Australia in Sydney –provide a competitive edge for hosts Great Britain?

Take a look at the full report to get some fascinating insights into how the medals could be shared out this year http://pwc.to/MvXf2x

Transcript of "PwC - Modelling Olympic performance"

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Modelling OlympicperformanceEconomic briefing paper 113 US 87 China 68 Russia 54 Great Britain Estimate of medal total at the 2012 London Olympic Games www.pwc.co.uk/economic-services

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Economic briefing paper: modelling Olympic performanceExecutive summaryAs we are getting closer to the start of the 2012 Olympic Games in London, so interest is rising inthe likely medal tallies of different countries. As a contribution to this debate, this paper presentsanalysis on the determinants of past Olympic performance and uses this to produce somebenchmarks against which performance at the 2012 Olympics can be judged. This updates similaranalysis we produced around the time of the 2000, 2004 and 2008 Olympics.The following economic and political factors were found to be statistically significant inexplaining the number of medals won by each country at previous Olympic Games: population; average income levels (measured by GDP per capita at PPP exchange rates); whether the country was previously part of the former Soviet/communist bloc (including Cuba and China); and whether the country is the host nation.In general, the number of medals won increases with the population and economic wealth of thecountry, but less than proportionately: David can sometimes beat Goliath in the Olympic arena,although superpowers like the US, China and Russia continue to dominate at the top of the medaltable.Many countries from the former Soviet bloc continued to outperform relative to the size of theireconomies at the Beijing Olympics, despite it being held nearly two decades years after the fall ofthe Berlin Wall. This effect is fading, however, and is no longer statistically significant if recentOlympic performance is also included in the model. We can, however, see a similar effect at workin China more recently, where state support contributed greatly to their Olympic success inBeijing: sport it seems is one area where a planned economy can succeed!Now it is no longer the host country, however, China may find it more difficult to stay ahead of theUS (as it did in Beijing on gold medals although not total medals). Indeed we find that hostnations generally ‘punch above their weight’ at the Olympics, which bodes well for the Britishteam in London.Our model suggests that the British team could win around 54 medals this time around, beatingan already exceptionally good performance of 47 medals in Beijing. This would still leave Britainin fourth place in the total medal table, behind the US (113 medals), China (87) and Russia (68),but ahead of old rivals Australia (42) and Germany (41) according to the model projections. Butall models are subject to margins of error and they can never take full account of the human factorof exceptional individual performances – so we will be only too pleased if the British team canbeat our model projection in London this summer! June 2012 PwC Page 1 of 10

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Economic briefing paper: modelling Olympic performanceEconomic briefing paper: modellingOlympic performanceWith the 2012 Olympic Games in London fast approaching, there will inevitably be muchspeculation about how many medals each country will win. In this paper we consider, as a light-hearted (but nonetheless reasonably rigorous) contribution to the debate, how far statisticalmodels can help to explain the number of medals won by each country in past Olympics. Wepublished the results of a similar modelling exercise around the time of the Beijing 2008, Athens2004 and Sydney 2000 Olympics and have now updated this analysis, taking into account alsothe results of other past studies in this area1.Key features of our modelThe updated version of our model includes data on medal performance from the Olympic Gamessince 20002. We find that, in explaining the share of the total medals awarded to each country,the following economic and political factors are statistically significant in a model that does notinclude past Olympic performance (see Annex for further technical details): population; average income levels (measured by GDP per capita at PPP exchange rates); whether the country was previously part of the former Soviet bloc (including Cuba in this case); and whether the country is the host nation.For predictive purposes, we also took account of performance in the previous two Olympics, asdiscussed further below and in the Annex.David vs GoliathIn the case of both population and average income levels, we found that the best fit3 was obtainedby using the logarithm of these variables as the explanatory factor, which implies that the number1 In particular, A.B. Bernard and M.R. Busse, ‘Who Wins the Olympic Games: Economic Resources and MedalTotals’, Review of Economics and Statistics, 2002; and D.K.N. Johnson and A. Ali, ‘A Tale of Two Seasons:Participation and Medal Counts at the Summer and Winter Olympic Games’, Wellesley College Working Paper2002-02, January 2002.2 In earlier versions of this paper we looked back to 1988, but on reviewing the data we felt that the mostinformation was contained in the more recent Games since 2000 when it came to setting medal targets for 2012.We have therefore focused on these last three Olympic Games in our statistical analysis. Our results are,however, broadly comparable to those of the studies quoted in the previous footnote, which do cover a longertime span.3 This was true for the variant of the model excluding performance in previous Olympics. When including theseadditional factors, we found that simply including the level of GDP in the model gave the best fit (see Annex fordetails). However, while such a model has greater predictive power, it is less interesting in terms of explaininghow economic and political factors influenced past Olympic performance. June 2012 PwC Page 2 of 10

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Economic briefing paper: modelling Olympic performanceof Olympic medals won rises less than proportionately as population and/or income levelsincrease. The coefficients on the population and income variables were similar, suggesting that itis total GDP that matters most in predicting Olympic performance rather than how this splitsdown between population size and average income levels. The less than proportionaterelationship between Olympic medal success and GDP implies that there are diminishing returnsfrom economic size in terms of increased sporting success. There are a number of possiblereasons for this: as discussed further below, we find clear statistical evidence that the former Soviet bloc countries significantly outperformed expectations based on their relatively low GDP levels; outstanding athletes from smaller countries may be able to train in wealthier countries (e.g. by attending US universities) but may continue to represent their own countries in the Olympics; for a small country, one or two gold medal performances from such athletes can make a large percentage difference to their overall points scores; this may well, for example, be a factor in the success of some Caribbean sprinters; while outstanding athletes in large countries like the US may be spread across a very wide range of Olympic and non-Olympic sports, there may be more of a tendency for athletes in smaller countries to specialise in a narrower range of disciplines where there is a local track record of success (e.g. long distance running for Kenya or sprinting for Jamaica); this strategy of specialisation can prove proportionately very successful in producing Olympic medals (and is perhaps analogous to the development of specialised industry ‘clusters’ in particular countries/regions, where a virtuous circle can then develop to generate world class performance4); and although this is difficult to prove, it could be that there is more focus on sport in some poorer countries where other life opportunities are more limited; if true, this greater motivation to participate and excel in sport may make up to some degree for inferior training facilities, at least in the early stages of a career.Whatever the explanation, the bottom line is that size matters, but it is not everything. David cansometimes slay Goliath in the Olympic arena.Long live the USSR – though the memory is fadingAs noted above we found, in common with previous studies and our own earlier analysis, thatwhether a country was formerly in the Soviet bloc (or is in a communist regime like China orCuba) was statistically significant5, given the high political importance of sporting success inmany of these countries. This shows that sport is one area where state planning and interventioncan produce results6, which still persisted in Beijing almost two decades after the fall of the Berlinwall. However, successive estimations of our model over the last four Olympics show that theseeffects are gradually fading for the ex-Soviet countries, except for China which still has a strong4 This cluster theory was first developed in detail by Michael Porter in his book, Competitive Advantage of Nations(1990). Perhaps he could include a review of sporting excellence clusters in the next edition?5 Except where past Olympic performance was included in the model, in which case this already capture this‘Soviet bloc’ effect (see Annex for details).6 Many would argue unfair results due to the well-documented use of performance-enhancing drugs by someSoviet bloc countries before the 1990s, but this is unlikely to be such a factor in relative performance now. June 2012 PwC Page 3 of 10

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Economic briefing paper: modelling Olympic performancestate policy of promoting Olympic sport. On the other hand the strong sporting traditions createdin these countries could last for some time yet, so there is still likely to be some outperformance inLondon by former Soviet bloc countries relative to what might have been expected based on theirGDP alone.We also found that it was worth distinguishing here between the group of ex-Soviet bloc orcommunist countries where a particularly high priority was given to sport (in particular, Russia,Ukraine, Poland, Bulgaria and Romania, as well as China and Cuba) and other ex-Soviet orplanned economies where this was less of a priority. For unified Germany, we included a dummyvariable value of 0.5 here to allow for the influence of the former East Germany.Home country advantage: good news for Britain in LondonWe also found the home country effect to be significant. In practice, however, this effect will varyacross countries depending on their size and the strength of their sporting traditions. It wasparticularly strong for China in Beijing (where its medal total rose to 100 from 63 in Athens) andwas also evident in the Sydney 2000 Olympics, where Australia performed very well to win 58medals (compared to 49 in Athens and 46 in Beijing as this effect faded). But it was somewhatless evident in Athens, where Greece only increased its medal total to 16, as compared to 13 inSydney (and then fell back to just 4 medals in Beijing).Overall, our model estimates suggest that home country advantage should on average boostmedal share by around 2 percentage points, which might translate to around 19 extra medals forBritain in 2012. However, this needs to be tempered by the fact that Beijing was already anexceptional performance for Britain that may have seen preparations for London 2012 alreadystarting to bear fruit in areas like cycling, rowing and sailing. So our overall model projection, asdiscussed further below, suggests a solid but more modest increase in Britain’s medal total to 54in London.History mattersFinally, we found that the explanatory power7 of the model was increased significantly byincluding medal shares at the previous two Games, which can be interpreted as reflecting the factthat sources of comparative advantage in sport tend to persist over time. Once this pastperformance is allowed for some other factors (particularly the ex-Soviet bloc effect) are no longerstatistically significant as they are already captured in past performance.Technical details of the model are discussed further in the Annex. It is not surprising that themodel cannot explain all the variation in medal shares across countries as this will also beinfluenced by individual athletic performances, as well as by policy-related factors such as: the relative level of state and corporate funding of Olympic athletes in each country (as a % of GDP); unfortunately we do not have data to hand on this, but comparatively high levels of corporate sponsorship may help to explain why the US medal share remains so high;7 The explanatory power of the preferred model was reasonably high, as indicated by an adjusted R-squared of0.96 (i.e. the model explains around 96% of the variance in medal shares between countries). If we exclude thelagged dependent variables, the explanatory power of the model drops to only around 50% (see Appendix fortechnical details). June 2012 PwC Page 4 of 10

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Economic briefing paper: modelling Olympic performance the relative effectiveness of this funding, which could reflect the extent to which it has been focused on building up successful sporting clusters of genuine world class, rather than being more widely dispersed across a range of different sports; it would also reflect the effectiveness of sports administrations in different countries; and the relative importance given to athletics and other Olympic sports where significant numbers of medals are at stake (e.g. swimming, cycling, sailing, shooting, amateur boxing and rowing), as opposed to other sports which are either not represented at the Olympics (e.g. golf, rugby, American football and cricket) or where relatively few medals are at stake (e.g. football and basketball). This is likely to be related to a complex mix of historical and cultural factors as, indeed, will be the importance given to sport per se in different societies.It follows that, if a country’s performance at the Olympics differs significantly from what oureconomic model would predict, this could have some policy implications in relation to the leveland effectiveness of sports funding as compared to other countries.Model estimates of medal targets for London 2012If we apply the model to the latest available data for each country, we obtain estimated medaltargets for London 2012 as shown in Table 1 (assuming that the total number of medals awardedis the same8 as in Beijing to allow direct comparison with results from 2008).These model estimates represent one possible benchmark or target against which to calibrate howwell a country does at the London Olympics given its size, income levels, political history and pastperformance. We would note in particular that: as host country, all eyes will be on Britain, where as noted above our model indicates a target for London of 54 medals, better than the already very good performance in Beijing due to the significant role that home advantage seems to play in Olympic performance; this has not been true for all hosts, however, as the example of the US in 1996 showed, so this is quite a challenging target for the Great Britain team to meet - but its strategy of specialising in key sports like cycling9, sailing and rowing should pay dividends again; China did well as the host nation in Beijing, topping the table on gold medals and not far behind the US on total medals won; however, despite its continued strong economic performance since 2008, there must be some question as to how far China can match its exceptionally good performance in Beijing now it no longer has the huge government push for success at its home Olympics; our model therefore suggests that China will remain in second place in the total medals table but a little further behind a resurgent US team than in 2008; but this is far from certain since there is still considerable state support for Chinese Olympic sport and many of the champions of 2008 will still be strong contenders in 2012;8 In practice, there could be some change in the total number of medals awarded in London, in which case themodel estimates in Table 1 would need to be adjusted pro rata. But there is unlikely to be a large change.9 Although we understand that there will be fewer medals to win in track cycling this time. June 2012 PwC Page 5 of 10

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Economic briefing paper: modelling Olympic performance Russia is projected by the model to continue to perform strongly relative to the size of its economy in third place (68 medals), but it does continue to drift down the table relative to the heights of its performance in the old USSR era; the two countries with by far the largest populations in the world are China and India, but their past Olympic performances could be not be more different: China is very strong as noted above, while India won only 3 medals in Beijing (though this was an improvement on just one medal in both Athens and Sydney); our model can explain some of this divergence, but still suggests that India is a significant underperformer, with a model target of around 5-6 medals for London. The most plausible explanation is that, with the exception of hockey, Indian sport tends to be focused on events that are not included in the Olympics, most importantly cricket; China, by contrast, is an example as noted above of the effectiveness of state planning in sport, comparable to the former Soviet bloc countries10; our model estimates suggest that larger Western European countries such as Germany, France, Italy, Spain and Netherlands might be expected to broadly match their Beijing 2008 performances in London, though they will no doubt hope to do better; countries where the model targets for London are below those in Beijing include Australia (still in gentle decline from the heights of Sydney in 2000) and some former Soviet bloc countries where the legacy advantages of strong state support from that pre-1991 era may be gradually fading such as Ukraine, Belarus and possibly also Cuba; as well as Great Britain, countries that the model suggests have the potential to do better than in Beijing include Japan, Brazil (in the run up to being the host country in 2016), Romania and Turkey; it will be interesting to see if they can improve their standings in London as the model suggests; and overall the model estimates suggest that the top 30 countries might be expected to win around 80% of all the medals awarded in London, which would actually be slightly below the 82-83% shares of the top 30 countries in Beijing, Athens and Sydney. This also broadly mirrors the shape of the global economy, in which the top 30 countries account for just over 80% of world GDP.It will be interesting to see how actual medal performance in London compares to thebenchmarks represented by the model estimates. We will revisit this question after the OlympicGames.John HawksworthChief Economist, PwC (UK)tel: +44 (0)207-213-1650e-mail: john.c.hawksworth@uk.pwc.com10 Chess (although not an Olympic sport) is another example of this phenomenon, as Chinese players (particularlywomen, where Xie Jun, Zu Chen, Xu Yuhua and Hou Yifan have all won world championship titles since 1991)have increasingly been challenging the dominance of former USSR states now that state support for chess hasbeen greatly reduced in Russia and other ex-Soviet countries. India has also been enjoying a chess boom overthe past decade, but this has been more due to younger players emulating the example of Vishy Anand (the worldchess champion since 2007) than to state support for chess. June 2012 PwC Page 6 of 10

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Economic briefing paper: modelling Olympic performance Annex: technical details of regression models Table 2 below shows two alternative regression equations that we have estimated. The first model variant excludes past Olympic performance from the set of explanatory variables and so provides a purer indicator of the ability to explain variations between countries purely on the basis of economic and political factors. The second model variant includes performance at the previous two Olympic Games as an additional independent variable and has much higher overall explanatory power (as indicated by the respective adjusted R-squared coefficients of 0.96 for the second model, as against 0.51 for the first model). This second model therefore forms the basis for the London 2012 medal estimates quoted in Table 1 above. Since the unadjusted model estimates for medal shares in London did not add up exactly to 100%11, a small scaling factor was applied to given the results shown in Table 1. Table 2: Alternative model specifications (dependent variable = % medal share) Explanatory variables Model without past Model with past Olympic Olympic performance performance variables variables Constant -0.02 0.0006 Log (population: millions) 0.0057 - (6.3) Log (GDP per capita at PPPs 0.0051 - : $000s) (4.2) Level of GDP at PPPs ($ trn) - 0.0022 (5.2) Ex-Soviet bloc dummy 0.012 Not significant (3.2) Host country dummy 0.06 0.02 (4.1) (5.0) Medal share in previous - 0.422 Olympic Games (3.5) Medal share in previous but - 0.353 one Olympics Games (3.2) Explanatory power 0.51 0.96 (adjusted R-squared) Standard error of model 0.013 0.004 Number of countries covered 101 101 Note: t-statistics shown in brackets for explanatory variables Source: PwC analysis using data from 101 medal-winning countries in 2000, 2004 and 2008 Olympics, plus IMF data on population and GDP per capita at PPP exchange rates. For the London projections in Table 1 above, the 2008 Beijing Olympics results were factored into this model together with the latest IMF GDP and population estimates for 2011.11 The unadjusted medal shares added up to around 102%, so these were all scaled down by a factor of 1.02 togive the published results with the total number of medals set to 958, the same as were awarded in Beijing. June 2012 PwC Page 8 of 10

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Economic briefing paper: modelling Olympic performance As indicated by t-statistics greater than 2, all explanatory variables in both model variants were statistically significant at the 99% confidence level. When the lagged dependent variable is added, however, we find that the level of GDP is the best economic variable to include while the ex-Soviet bloc dummy is no longer statistically significant because it is already captured in the past performance variables. The home country advantage variable remains highly significant statistically but smaller in magnitude than in the first model (where it is actually implausibly large). The explanatory power of the second model rises significantly and the standard error of the model is reduced by more than half. In general, the first model is most interesting as a guide to the underlying economic and political drivers of past Olympic performance, while the second model is better for setting benchmarks against which to assess current and future Olympic performance. We have therefore used the second model to derive the medal projection results in Table 1 above, but the results of the first model are also discussed in some detail in the main text. Figure 1 below gives a visual indication of how closely our preferred second model fits the actual Beijing 2008 results. We can see from this chart that there are a large number of countries clustered around the bottom left of the chart who won only a few medals and this is in line with the model estimates. The model also fits well the performance of China and the US at the top right of the chart, although Russia was a slight underperformer in Beijing relative to what the model would have suggested, as was Germany. The largest ‘outperformer’ in Beijing, however, was Great Britain, whose medal haul of 47 was well above the model estimate of around 30. France outperformed but by a smaller margin than Great Britain, while Australia delivered a par performance according to the model. Figure 1: How well does our model fit the actual models won in Beijing? Figure 1: How well does our model fit the actual medals won in Beijing? 120 US China 100 Actual medals won in Beijing Olympics 80 Russia 60 Australia GB France 40 Germany 20 0 0 20 40 60 80 100 120 Model estimate of medals won by country in Beijing June 2012 PwC Page 9 of 10